"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=13      ,					_lowerCamelCase=3      ,					_lowerCamelCase=224      ,					_lowerCamelCase=30      ,					_lowerCamelCase=400      ,					_lowerCamelCase=True      ,					_lowerCamelCase=None      ,					_lowerCamelCase=True      ,					_lowerCamelCase=[0.5, 0.5, 0.5]      ,					_lowerCamelCase=[0.5, 0.5, 0.5]      ,					):
										lowerCamelCase__   =size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =num_channels
										lowerCamelCase__   =image_size
										lowerCamelCase__   =min_resolution
										lowerCamelCase__   =max_resolution
										lowerCamelCase__   =do_resize
										lowerCamelCase__   =size
										lowerCamelCase__   =do_normalize
										lowerCamelCase__   =image_mean
										lowerCamelCase__   =image_std






				def 						_a      ( self ):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}






@require_torch
@require_vision
class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):
				A__ :  int    				=			ViTImageProcessor if is_vision_available() else None

				def 						_a      ( self ):
										lowerCamelCase__   =EfficientFormerImageProcessorTester(self )

				@property
				def 						_a      ( self ):
										return self.image_proc_tester.prepare_image_processor_dict()

				def 						_a      ( self ):
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"image_mean" ) )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"image_std" ) )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"do_normalize" ) )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"do_resize" ) )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"size" ) )

				def 						_a      ( self ):
										pass

				def 						_a      ( self ):
										# Initialize image_processor
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										lowerCamelCase__   =prepare_image_inputs(self.image_proc_tester      ,					equal_resolution=_lowerCamelCase )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase      ,					Image.Image )

										# Test not batched input
										lowerCamelCase__   =image_processor(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

										# Test batched
										lowerCamelCase__   =image_processor(_lowerCamelCase      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

				def 						_a      ( self ):
										# Initialize image_processor
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										lowerCamelCase__   =prepare_image_inputs(self.image_proc_tester      ,					equal_resolution=_lowerCamelCase      ,					numpify=_lowerCamelCase )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase      ,					np.ndarray )

										# Test not batched input
										lowerCamelCase__   =image_processor(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

										# Test batched
										lowerCamelCase__   =image_processor(_lowerCamelCase      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)






				def 						_a      ( self ):
										# Initialize image_processor
										lowerCamelCase__   =self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										lowerCamelCase__   =prepare_image_inputs(self.image_proc_tester      ,					equal_resolution=_lowerCamelCase      ,					torchify=_lowerCamelCase )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase      ,					torch.Tensor )

										# Test not batched input
										lowerCamelCase__   =image_processor(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)

										# Test batched
										lowerCamelCase__   =image_processor(_lowerCamelCase      ,					return_tensors="pt" ).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,					(
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )      ,					)



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square(__lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												# BASE CASE
												if row >= rows or col >= cols:
																		return 0

												lowerCamelCase__   =update_area_of_max_square(__lowerCAmelCase  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						update_area_of_max_square(0  ,       0							)
						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square_using_dp_array(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												if row >= rows or col >= cols:
																		return 0
												if dp_array[row][col] != -1:
																		return dp_array[row][col]

												lowerCamelCase__   =update_area_of_max_square_using_dp_array(__lowerCAmelCase  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		lowerCamelCase__   =sub_problem_sol
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						lowerCamelCase__   =[[-1] * cols for _ in range(__lowerCAmelCase							)]
						update_area_of_max_square_using_dp_array(0  ,       0  ,       __lowerCAmelCase							)

						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[[0] * (cols + 1) for _ in range(rows + 1							)]
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =dp_array[row][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(dp_array[row][col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0

						return largest_square_area





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =current_row[col + 1]
																		lowerCamelCase__   =next_row[col + 1]
																		lowerCamelCase__   =next_row[col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(current_row[col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0
												lowerCamelCase__   =current_row

						return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


